Introduction and plan Domain of research



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  • Distributed Artificial Intelligence and Ontology-based Systems for Knowledge Management.


Introduction and plan

  • Domain of research:

    • Distributed (formal) knowledge
    • Distributed (artificial) intelligence
  • PhD research: “D. A. I. and knowledge”

    • Semantic intrawebs for corporate memories
    • Ontology engineering for semantic intrawebs
    • Multi-agent architecture for semantic intrawebs
  • Post-doc research: “D. A. I. and services”

    • Multi-agent architecture & Semantic Web services
    • Context-awareness for mobile accesses
    • Knowledge-based privacy enforcement
  • Wrap-up



Web to humans



Web to machines...



Positioning (semantic Web)

  • Internet & Web (shared infosphere for humans)

  • Intranets & intrawebs

    • Current trend: reuse internet and web technologies
    • Same advantages and same drawbacks
  • Semantic Web (shared infosphere for machines)

    • XML: W3C standard for structuring data and documents
    • RDF(S): W3C standard for metadata/semantic annotations
  • Hyp: corporate memories as semantic intrawebs



Positioning (ontology)

  • Knowledge engineering & ontology (modularity, reuse)

    • Assertional knowledge e.g., "Hugo wrote Notre-Dame de Paris"
    • Ontological knowledge e.g., "Authors write books
    • Ontology: an explicit, partial account of the semantic structure encoding the rules that constrain our representation of reality
  • Hyp: ontology to support semantic intraweb



Positioning (DAI) and context (CoMMA)

  • Distributed artificial intelligence

    • Multi-agent systems: study design of artificial societies of intelligent agents
    • Agent: clearly identifiable artificial entity; situated in an environment which it senses, reacts to and acts upon;
    • self-control of its behavior; social abilities to interact with other agents / humans;
    • Multi-agent information systems: situated in & distributed over an information network
  • Hyp: multi-agent system to manage semantic intraweb



European project CoMMA

  • CoMMA Corporate Memory Management through Agents Atos-Origin, CSELT Telecom Italia, CSTB, INRIA, LIRMM, T-Nova Deutsche Telekom, University of Parma

  • Two application and trial scenarios:

    • Assist new employee integration
    • Support technology monitoring activities
  • Technical choices:

    • Materialization of memory RDF(S) and its XML syntax (manipulated with CORESE)
    • Exploitation of memory Multi-agent system and machine learning techniques (implemented with ) (implemented with WEKA)


Positioning and pointers

  • Dynamically integrating heterogeneous sources of information OBSERVER [Mena et al., 1996] InfoSleuth [Nodine et al., 1999] Carnot [Collet et al., 1991] InfoMaster [Genesereth et al., 1997] SIMS [Arens et al., 1996] RETSINA [Decker & Sycara, 1997] Manifold [Kirk et al.,1995]

  • Assist the management of digital libraries SAIRE [Odubiyi et al., 1997] UMDL [Weinstein et al., 1999]

  • Organizational knowledge management:

    • Collaborative gathering, filtering and profiling CASMIR [Berney & Ferneley, 1999] Ricochet [Bothorel & Thomas, 1999]
    • Mobile access, domain model, document classification KnowWeb [Dzbor et al., 2000]
    • Taxonomy of topics, profiling and push RICA [Aguirre et al., 2000]
    • Ontology and corporate memory: multiple ontologies FRODO [Van Elst & Abecker, 2001] semantic intraweb, ontology, user profiling You are here


Functionalities: annotate, pull and push



Annotate documents (content awareness)

  • RDF(S): annotated world for software in order to make inferences & help users exploit memory

    • RDF = Resource Description Framework (annotation model)
      • Annotation in RDF with XML syntax:
  • Document rdf:about=“http://www-sop.inria.fr/aar.doc”>

  • Title>Annual activity report of ACACIA

  • Author>

  • Person rdf:about=“http://www.inria.fr/~rdieng/” />

      • Annotation in RDF with graph representation:


Annotation schema (semantic awareness)

  • RDF(S): annotated world for software in order to make inferences & help users exploit memory

    • RDF = Resource Description Framework (annotation model)
    • RDFS = RDF Schema (annotation vocabulary / ontology)
  • Class rdf:ID=‘Entity’/>

  • Class rdf:ID=‘Document’>

  • subClassOf rdf:resource=‘#Entity’ />

  • ...

  • Property rdf:ID=‘Author’>

  • subPropertyOf rdf:resource=‘#Creator’ />

  • domain rdf:resource=‘#Document’ />

  • range rdf:resource=‘#Person’ />

  • ...

      • In graph representation:


Annotate persons (context awareness)

  • User modeling (extract of my user profile)

  • Engineer rdf:about="http://www-sop.inria.fr/acacia/personnel/Fabien.Gandon/">

  • FamilyName>GANDON

  • FirstName>Fabien

  • BirthDate>31-07-1975

  • (...)

  • HasForWorkInterest>

  • MultiAgentSystemTopic rdf:about="http://www.inria.fr/acacia/comma#...

  • (...)

  • HasForPersonalInterest>

  • HumanScienceTopic rdf:about="http://www.inria.fr/acacia/comma#Human...

  • (...)

  • Employee rdf:about="http://www-sop.inria.fr/acacia/personnel/Fabien.Gandon/">

  • HireDate>1999-11-02

  • EmployedBy>

  • LocalOrganizationGroup rdf:about="http://www.ac-nice.fr/" />

  • EmploymentContract> Temporary/>

  • HasForActivity>

  • Research rdf:about="http://www.inria.fr/acacia/comma#Research"/>

  • Idem for organization modeling (structures, relations...)



Progression

  • PhD research: “D.A.I. and knowledge”

    • Semantic intrawebs for corporate memories
    • Ontology engineering for semantic intrawebs
    • Multi-agent architecture for semantic intrawebs
  • Post-doc research: “D.A.I. and services”

    • Semantic Web services &Context-awareness
    • Personal resource management & privacy enforcement
    • Experimental results
  • Wrap-up



Methodological steps (1 & 2)

  • Ontology building in five steps

  • Step 1 - Data collection and analysis

    • Scenario-driven analysis: users’ scenario reports & grid
    • Motivate data collection internal & external to organization
    • Capture aspects conceptualization to assist scenarios
  • Extract: “... wonder if there are technical reports about UMTS, then...” “... what this manager or one of his colleagues wrote for...”

  • Step 2 - Build a lexicon

    • Capture terms and their definitions
    • First intermediary representation of the ontology
    • Constraint: one and only one occurrence of a definition
    • Disambiguate terms, e.g.:
    • COLLEAGUE n. (lat. collega) someone who shares the same profession || one of a group of people who work together.


Methodological steps in ontology engineering (3)

  • Step 3 - Enriching lexicon structure

    • Split concepts, properties and attributes into different tables
    • Augment with relevant semantic aspects (e.g. subsumption)
    • Enrich, augment, refine, ... for both humans and machines
    • Taxonomic skeleton: top-down / bottom-up / middle-out
  • Step 4 - Script translating tables into RDFS



Analysis of the three levels present in RDF(S)



Methodological steps in ontology engineering (5)

  • Step 5 - Factorizing knowledge (when needed)

    • Declare algebraic properties of relations (symmetric / transitive / reflexive relations)
  • colleague(x,y) acquaintance(x,y)  colleague(y,x)

  • true

  • acquaintance between two persons

  • who work together.

  • accointance entre deux personnes

  • travaillant ensemble.

  • colleague

  • co-worker

  • collegue

  • collegue de travail



Methodological steps in ontology engineering (5)

  • Step 5 - Factorizing knowledge (when needed)

    • Declare algebraic properties of relations (symmetric / transitive / reflexive relations)
    • No one generates all the instances of colleague by hand
    • "I am a colleague of X because I work in the same group as X" (inference)
    • Encode axiomatic knowledge in rules and definitions
  • colleague(x,y) person(x)  person(y)  (z group(z)  include(z,x)  include(z,y))

  • IF (rule for sufficient condition)

  • Group

  • Include

  • Person ?x

  • Include

  • Person ?y

  • THEN

  • Person ?x

  • Colleague

  • Person ?y



Summary of methodology

  • Results

    • Design stages:
    • In CoMMA this method provided O'CoMMA
      • 470 concepts (taxonomy depth = 13 subsumptions).
      • 79 relations (taxonomy depth = 2 subsumptions).
      • 715 terms in English and 699 in French.
      • 550 definitions in English and 547 in French .


Resulting ontology: O'CoMMA

  • Structure:

    • Abstract top & middle layer for corporate memory: reusable
    • Middle layer for domain: reusable in same domain
    • Extension layer: usable but not reusable
    • Reuse tested e.g., CSELT  CSTB, APROBATIOM, KMP


Progression

  • PhD research: “D.A.I. and knowledge”

    • Semantic intrawebs for corporate memories
    • Ontology engineering for semantic intrawebs
    • Multi-agent architecture for semantic intrawebs
  • Post-doc research: “D.A.I. and services”

    • Semantic Web services &Context-awareness
    • Personal resource management & privacy enforcement
    • Experimental results
  • Wrap-up



A multi-agent architecture for CoMMA

  • Problem = handle information distribution

    • Handle naturally scattered information & knowledge
    • Assist diffusion of captured information and knowledge
  • Follow multi-agent paradigm:

    • Artificial societies collaborating for global capitalization
    • Artificial individual intelligence, able to locally adapt
  • Step 1 - Sub-societies identification

    • Started from the tasks to be performed (provide ontology, manage annotations, manage users and matchmaking)
    • Thus four sub-societies to handle these four tasks:


Organizing resource-dedicated sub-societies

  • Step 2 - Analyse possible organization structures:



Roles and interactions



Zooming on annotation-dedicated sub-society



Annotation-dedicated sub-society (mediator)



Annotation-dedicated sub-society (archivist)



Interaction mediator-archivist (allocation)

  • Interaction 1 - Annotation allocation

    • Pb: archives distributed all over organization
    • Mediator & archivists discuss best archive for new annot.
    • Contract-net (CfP, Proposal, Accept/Reject):
    • Proposals: semantic distance new annotation - archive


Content of an annotation or of an archive

  • Simple annotation

  • Article rdf:about="http://intranet/reports/R3029">

  • Title>CfP UMTS Analysis

  • Author>

  • Person rdf:about="http://www.mycorp.com/~fab" />

  • Corresponding triples:

  • Unstructured set of triples to describe content type



Distance between an annotation and an archive



Building the distance

  • Case 1 - literal values: lexicographical distance

  • Case 2 - concept types: minimum length of path between two types through least common super type

  • DistH(Type1,Type2) = Min(GPath(Type1,LCST)+GPath(Type2,LCST)) GPath(,): number of edges through generalization links LCST: least common super type = shared characteristics

  • Distance between two triples (conditional weighted sum)



Semantic distance between types

  • Distance = allocation criteria of contract-net

    • "and the winner is..." the archivist with smallest distance
    • Cluster annotations & specialize archives
    • Improve query solving & respect knowledge distribution


Interactions mediator-archivist in solving a query



Overlap description



RDF query: tree structure



Decomposition



Decomposition



Overview of agent-based design

  • Design stages:

  • Result:

      • Society providing ontology and corporate model
      • Annotation-dedicated society
      • Two trials at mid-project and project end in development spiral
      • One public open-day demo: industrial & EU commission
      • Usability and usefulness recognized by users/public


Progression

  • PhD research: “D.A.I. and knowledge”

    • Semantic intrawebs for corporate memories
    • Ontology engineering for semantic intrawebs
    • Multi-agent architecture for semantic intrawebs
  • Post-doc research: “D.A.I. and services”

    • Semantic Web services &Context-awareness
    • Personal resource management & privacy enforcement
    • Experimental results
  • Wrap-up



Web service to humans



Web service to machines



Positioning (semantic Web services)

  • Internet & Web (shared service-sphere for humans)

  • Semantic Web (shared infosphere for machines)

  • Semantic Web Services (shared service-sphere for machines)

    • SOAP, XMLP, WSDL, DAMLS, UDDI (v2  v3)
  • Hyp: Semantic web services for e-Business and Enterprise Applications Integration

  • Hyp: Ontology to support semantic Web services

  • Distributed artificial intelligence

    • Intelligent Agent  Intelligent Services
    • Multi-agent systems  Service interaction & composition
    • Hyp: Multi-agent architectures for webs of services


Interface without context awareness



Interface with context awareness



Positioning (mobile access to SW&S)

  • Mobile (net)working

    • Mobile networks:
      • Phones / pagers  PDAs, sub-notebooks
      • Telecom services  Web and online services
    • Mobile accesses  reduced & constrained interactions (interfaces, connectivity, cognitive workload)
    • Schism: service availability & user availability
  • Hyp: Context-awareness to support mobile access to semantic Webs and their services



Positioning (context awareness)

  • Context awareness State of the Art

    • Application leveraging context awareness Active Badge [Want et al., 92] ParcTab [Schilit, 95] Oxygen [Dertouzos, 99] GUIR [Hong & Llanday, 01] Aura [Garlan et al., 02]
      • Application dependent and heterogeneous
      • Redundant and scattered
    • Personal resources integration and unification
      • Toolkits and widgets for wrapping [Dey et al., 00]
      • e-Wallet: awareness & privacy You are here
  • Hyp: Semantic Web & Services to provide a unified secure interface to personal resources (e-Wallet)



Context of research

  • myCampus: a context-aware environment aimed at enhancing access to services for everyday campus life at Carnegie Mellon University (CMU)

    • BBN, IBM, HP, Symbol and Boeing
    • Air Force Research Laboratory (contract F30602-02-2-0035)
    • Defense Advanced Research Project Agency (DARPA) (contract F30602-98-2-0135)
  • Interactions with

    • SONAT: user-aware notification (D.o.D.)
    • CoSAR (I-X, KAoS/CoABS Grid): notification planning (AIAI)
    • SWAP: Semantic Web and Peer-to-peer for KM (IST program)
  • My focus: Semantic Web & Services, Context-awareness and Privacy, M.A.S. and Ontologies



Open architecture – mobile access

  • PDA & Wireless Network

  • Agent roles:

    • Platform manager
    • User interaction manager
    • Growing collection of task-specific agents
    • e-Wallet manager
  • Web resources

    • Semantic Web services
    • Semantic Web ontologies
    • Semantic Web annotations
    • Search engines


Progression

  • PhD research: “D.A.I. and knowledge”

    • Semantic intrawebs for corporate memories
    • Ontology engineering for semantic intrawebs
    • Multi-agent architecture for semantic intrawebs
  • Post-doc research: “D.A.I. and services”

    • Semantic Web services &Context-awareness
    • Personal resource management & privacy enforcement
    • Experimental results
  • Wrap-up



e-Wallet & the knowledge typology

  • Secured unified semantic interface to access knowledge about the e-Wallet owner

  • Static assertional knowledge:

    • User’s static profile (name, SSN, position, etc.)
    • Static contextual knowledge (campus description, etc.) fetched from outside as needed.
  • Dynamic assertional knowledge:

    • User’s dynamic profile (contextual preferences, etc.)
    • Context knowledge (location, current activity, weather, etc.) fetched from outside as needed.
    • Privacy: authorization & obfuscation (precision, lie, etc.)
  • Ontological knowledge, fetched from an outside repository of ontology at startup.



Design of an e-Wallet

  • Three-layer architecture

    • Core knowledge: static & dynamic knowledge of user
    • Service Layer: invoke external sources of knowledge: web services and personal resources
    • Privacy layer: enforce privacy rules on external requests: access, obfuscation (precision, lies)


Processes in the e-Wallet



e-Wallet core: JESS inference engine

  • CLIPS language:

    • Atoms, numbers, strings, functions, variables, Java reflection
    • Ordered facts (not used here)
    • Unordered facts (with templates // classes)
    • Forward rules
    • Queries: special kind of rule with no right-hand-side
    • Declare backward chaining reactive rules using (do-backward-chaining …) function (template  need-template)
    • The RETE algorithm


e-Wallet semantic engine

  • RDF Triple model

  • RDFS & OWL meta-model (e.g, symmetry of properties)



e-Wallet semantic engine

  • Ontologies: (e.g., declare man, location, etc.)

  • Annotations: (e.g., Fabien is in Smith Hall, etc.)



e-Wallet semantic engine

  • Rules: (e.g., when in I am in a meeting I am busy)



e-Wallet semantic engine

  • Service and privacy layers

    • Special types of triples
    • Backward chaining reaction started by the query
      • Privacy rules (clearance/revision)
      • Service rules (dynamic access)
      • Static migration rules


Query

  • Three steps:

    • Query context assertion
    • Query rule definition
      • Body: request for authorized triples
      • Head: storage & pretty printing function
    • Rule execution


Privacy rules



Service rules



Progression

  • PhD research: “D.A.I. and knowledge”

    • Semantic intrawebs for corporate memories
    • Ontology engineering for semantic intrawebs
    • Multi-agent architecture for semantic intrawebs
  • Post-doc research: “D.A.I. and services”

    • Semantic Web services & Context-awareness
    • Personal resource management & privacy enforcement
    • Experimental results
  • Wrap-up



Prototype #1: proof of concept

  • Mockup of multi-agent architecture

  • Two task-specific agents

    • Restaurant Concierge Agent RCA (location, weather, food, price, time)
    • Message Filtering Agent (interests, current activity) + Jabber (Instant Messaging)
  • Limited version of e-Wallet

  • JSP pages to simulate User Interface Manager

  • Three Web services

    • Location tracking
    • Calendar access
    • Weather report


Setting preferences



Using services





Experiment #1 with early prototype

  • Before the experiment:

    • Office for Human Research Protections approval Institutional Review Board certificate
    • Selected group of 11 users, with a variety of profiles
    • Trained the 11 users during a 2-hour session + material
  • The 3-day experiment involved:

    • Message filtering agent: 44 messages and 484 feedbacks
    • Restaurant concierge agent: 28 recommendations
    • Logs were generated for each one of these events.
  • After the experiment:

    • Users had to fill a survey ~1/2 hour
    • Face-to-face de-briefing interviews ~15 minutes
    • Statistics on the logs


Experiment #1 extracts of feedback analysis



Experiment #1 most important results

  • For the Restaurant Concierge, 12.5 % recommendations accepted thanks to context-awareness = 14.29% improvement.

  • For the Messaging: filtering and routing based on static profile = too loose.

    • finer knowledge about the users and finer filtering criteria;
    • ~70% of the messages benefit from context-aware filtering and routing e.g. messages to be sent only when available or when the day is over
  • Need critical mass of content, users and useful services



Prototype #2: study of a useful service

  • Focus:

    • Find out about interesting events & Get information to people who want it
    • Existing systems to distribute information?
    • Design Computer Science Information Sc. Masters Electrical Eng.


Experiment #2 H.C.I. study of a useful service

  • Improving messaging for events:

    • Tag each poster with a list of locations (situated)
    • Display poster when user is close to it or explicitly interested in the topic (distribute)
    • Peer-to-peer publishing (person-2-person)
    • Integrate with calendar (remember)
    • Stop publishing when outdated (maintenance)
    • Under evaluation; good feedback


Progression

  • PhD research: “D.A.I. and knowledge”

    • Semantic intrawebs for corporate memories
    • Ontology engineering for semantic intrawebs
    • Multi-agent architecture for semantic intrawebs
  • Post-doc research: “D.A.I. and services”

    • Semantic Web services &Context-awareness
    • Personal resource management & privacy enforcement
    • Experimental results
  • Wrap-up



Wrap-up: conclusion

  • Ph.D. contributions

    • Methodologies and tools to build ontology, semantic intraweb and associated multi-agent architecture
    • A running prototype as a proof of concept for each point
    • Both are reusable contributions
    • Current extensions: Web scrappers & Semantic gateways
  • Post-Doc contributions ( as a continuation)

    • Design M.A.S. architecture for Semantic Web Services with a focus on mobile accesses
    • Use of S.W. and ontologies to allow context-awareness
    • Integration S.W. and ontologies in privacy enforcement


Wrap-up: discussion

  • Research ahead (remain at the state of the art)

    • Ontology and Memory life cycles (collective-ware, coherence)
    • Semantic Webs (semantic mapping, open/extra, community, security)
    • Semantic Services and Agents (EAI, e-Sourcing, Grid, autonomic)
    • Mobile & ubiquitous Semantic Web (connectivity, pervasiveness)
    • Intelligent user interfaces (customization, awareness, ergonomics)
  • Perfectly integrated with research of ACACIA & INRIA



Acknowledgments

  • ACACIA Laboratory - INRIA Sophia Antipolis

    • Dr. Rose Dieng-Kuntz (Research Director – ACACIA project leader)
    • Members of ACACIA team and logistics of INRIA Sophia
    • ATOS-Origin, CSTB, Deutsch Telekom T-Nova, Italia Telecom, LIRMM and University of Parma
    • IST Program (CoMMA project)
  • School of computer science – Carnegie Mellon Uni.

    • Prof. Norman M. Sadeh (Mobile Commerce Laboratory Director, SCS CMU, Free University of Amsterdam, European Commission)
    • Members of myCampus team and logistic of ISRI
    • BBN, IBM, HP, Symbol and Boeing
    • IST Program (SWAP project)
    • Air Force Research Laboratory (contract F30602-02-2-0035)
    • Defense Advanced Research Project Agency (DARPA) (contract F30602-98-2-0135)


Demo: CoMMA Ontology interface



CoMMA: profile-based retrieval



Demo: interface CoMMA



Demo: CoMMA annot. allocation interactions



Demo: CoMMA query solving interactions



Demo: InfoBridge virtual posters





Acknowledgments

  • ACACIA Laboratory - INRIA Sophia Antipolis

    • Dr. Rose Dieng-Kuntz (Research Director – ACACIA project leader)
    • Members of ACACIA team and logistics of INRIA Sophia
    • ATOS-Origin, CSTB, Deutsch Telekom T-Nova, Italia Telecom, LIRMM and University of Parma
    • IST Program (CoMMA project)
  • School of computer science – Carnegie Mellon Uni.

    • Prof. Norman M. Sadeh (Mobile Commerce Laboratory Director, SCS CMU, Free University of Amsterdam, European Commission)
    • Members of myCampus team and logistic of ISRI
    • BBN, IBM, HP, Symbol and Boeing
    • IST Program (SWAP project)
    • Air Force Research Laboratory (contract F30602-02-2-0035)
    • Defense Advanced Research Project Agency (DARPA) (contract F30602-98-2-0135)


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